Probabilistic Structural Equations Models with Bayesian Networks

Probabilistic Structural Equation Models (PSEM), based on machine-learned Bayesian networks, provide an efficient alternative to traditional Structural Equation Models (SEM).

With BayesiaLab 5.0, PSEMs can be created through a series of semi-automatic clustering steps, which allow analysts to perform driver analysis extremely quickly, reducing research time from "months to minutes." This webinar will demonstrate a complete workflow for a typical application.

Dr. Lionel Jouffe and Stefan Conrady will present several updates to the approach originally described in their white paper, Driver Analysis & Product Optimization (http://www.conradyscience.com/index.php/driver-analysis). This will include an illustration of Direct Effects computed by means of Likelihood Matching.

Category:

Science & Technology

Tags:

• Bayesian network
• Structural Equation Model
• Causality
• SEM